RAG-Based Medical Chatbot (MediBot AI)
Delivering trustworthy, explainable AI for healthcare professionals and patients
Key Results
Measurable impact delivered through our solution
The Challenge
Healthcare organizations face rising patient queries, staff overload, and unreliable AI responses when using generic chatbots. There was a need for a secure, domain-specific conversational AI that could provide accurate, reference-backed guidance.
High patient query volume overwhelming medical staff
Generic chatbots producing hallucinated or unsafe responses
Lack of transparency and citation in AI-generated outputs
Difficulty integrating structured medical data with LLMs
Our Solution
We developed MediBot AI — a Retrieval-Augmented Generation (RAG) powered medical assistant that merges knowledge retrieval with LLM reasoning to provide context-aware, evidence-based responses.
Implemented RAG pipeline using pgvector in PostgreSQL for context retrieval
Integrated transformer-based Sentence-BERT and LLM for conversational accuracy
Added confidence scoring and citation features to enhance transparency
Built user interface with login, chatbot interaction, and follow-up queries
Technology Stack
Business Impact
MediBot AI reduced patient support load, enhanced medical accessibility, and delivered safe, explainable AI-driven assistance for healthcare professionals.
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